论文标题
双流多实例学习网络，用于整个幻灯片图像分类，并与自我监督的对比度学习
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
论文作者
论文摘要
我们解决了整个幻灯片图像（WSI）分类的具有挑战性的问题。 WSI具有很高的分辨率，通常缺乏局部注释。当仅幻灯片级标签可用时，WSI分类可以作为多个实例学习（MIL）问题。我们提出了一种基于MIL的WSI分类和肿瘤检测方法，该方法不需要局部注释。我们的方法具有三个主要组成部分。首先，我们介绍了一个新颖的MIL聚合器，该集合器在具有可训练距离测量的双流架构中建模实例的关系。其次，由于WSIS可以产生大型或不平衡的袋子，从而阻碍MIL模型的培训，因此我们建议使用自欺欺人的对比度学习来为MIL提取良好的表示，并减轻大型袋子的过度记忆成本问题。第三，我们为多尺度WSI特征采用了锥体融合机制，并进一步提高了分类和本地化的准确性。我们的模型在两个代表性的WSI数据集上进行了评估。我们模型的分类精度与完全监督的方法相比，数据集的精度差距不到2％。我们的结果也优于所有以前的基于MIL的方法。标准MIL数据集的其他基准结果进一步证明了MIL聚合器在一般MIL问题上的出色性能。 GitHub存储库：https：//github.com/binli123/dsmil-wsi
We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems. GitHub repository: https://github.com/binli123/dsmil-wsi
